Feature selection and the class imbalance problem in predicting protein function from sequence

Applied Bioinformatics

Volumn 4, Issue 3, 2005, Pages 195-203

  Al Shahib, Ali ^a,c   Breitling, Rainer ^a,b   Gilbert, David ^a  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

^b UNIVERSITY OF GLASGOW   (United Kingdom)

^c UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; COMPARATIVE STUDY; PERFORMANCE; PREDICTION; PROTEIN FUNCTION; SEQUENCE HOMOLOGY; TRAINING;

EID: 27344448597     PISSN: 11755636     EISSN: 11755636     Source Type: Journal    
DOI: 10.2165/00822942-200504030-00004     Document Type: Article

References (31)

1. Altschul SF, Madden TL, Schaffer AA, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997; 25: 3389-402
2. Whisstock JC, Lesk AM. Prediction of protein function from protein sequence and structure. Q Rev Biophys 2003; 36 (3): 307-40
3. King RD, Karwath A, Clare A, et al. Accurate prediction of protein functional class in the Mycobacterium tuberculosis and Escherichia coli genomes using data mining. Yeast 2000; 17: 283-93
4. Jensen R, Gupta H, Staerfeldt H, et al. Prediction of human protein function according to Gene Ontology categories. Bioinformatics 2003; 19: 635-42
5. Japkowicz N, Stephen S. The class imbalance problem: a systematic study. Intell Data Anal J 2002; 6 (5): 429-49
6. Drummond C, Holte RC. C4.5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. Workshop on Learning from Imbalanced Datasets II; 2003 Jul 21; Washington, DC
7. Estabrooks A, Jo T, Japkowicz N. A multiple resampling method for learning from imbalanced data sets. Comput Intell 2004; 20 (1): 18-36
8. An A, Cercone N, Huang X. A case study for learning from imbalanced data sets: advances in artificial intelligence. Proceedings of the 14th Conference of the Canadian Society for Computational Studies of Intelligence; 2001 June; Ottawa (ON). London: Springer-Verlag, 2001: 1-15
9. John GH, Langley P. Estimating continuous distributions in Bayesian classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. Montreal (QC): Morgan Kaufmann, 1995: 399-406
10. Quinlan JR. C4.5: programs for machine learning. San Mateo (CA): Morgan Kaufmann, 1993
11. World Health Organization. Global prevalence and incidence of selected curable sexually transmitted infections, overview and estimates. Geneva: World Health Organization, 2001
12. Arvidson CG, Powers T, Walter P, et al. Neisseria gonorrhoeae PilA is an FtsY homolog. J Bacteriol 1999; 181: 731-9
13. Riley M. Functions of the gene products of Escherichia coli. Microbiol Rev 1993; 57: 862-952
14. Bishop CM. Neural networks for pattern recognition. Oxford: Oxford University Press, 1993
15. Coissac E, Maillier E, Netter P. A comparative study of duplications in bacteria and eukaryotes: the importance of telomeres. Mol Biol Evol 1997; 14 (10): 1062-74
16. Vapnik V. Statistical learning theory. New York: John Wiley & Sons Inc., 1998
17. Platt JC. Fast training of support vector machines using sequential minimal optimisation. In: Scholkopf B, Burges C, Smola A, editors. Advances in kernel methods: support vector learning. Cambridge (MA): MIT Press, 1999: 185-208
18. Witten IH, Frank E. Data mining: practical machine learning tools and techniques with java implementations. San Francisco (CA): Morgan Kaufmann, 2000
19. Dash M, Liu H. Feature selection for classification. Intell Data Anal J 1997; 1 (3): 131-56
20. Kohavi R, John GH. Wrappers for feature subset selection. Artif Intell 1997; 97 (1-2): 273-324
21. Doak J. An evaluation of feature selection methods and their application to computer security. Davis (CA): Department of Computing Science, University of California at Davis, 1992. Technical report no.: CSE-92-18
22. John GH, Kohavi R, Pfleger K. Irrelevant features and the subset selection problem. Proceedings of the 11th International Conference on Machine Learning. San Mateo (CA): Morgan Kaufmann, 1994: 121-9
23. Weiss GM, Provost F. The effect of class distribution on classifier learning: an empirical study. New Jersey: Department of Computer Science, Rutgers University, 2001. Technical report no.: ML-TR-44
24. Al-Shahib A, He C, Tan AC, et al. An assessment of feature relevance in predicting protein function from sequence. In: Proceedings of the Fifth International Conference on Intelligent Data Engineering and Automated Learning (IDEAL' 04). Lecture Notes in Computer Science. Volume 3177. Exeter: Springer-Verlag, 2004: 52-7
25. Bamber D. The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. J Math Psychol 1975; 12: 387-415
26. Gribskov M, Robinson NL. Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching. Comput Chem 1996; 20: 25-33
27. Huang J, Jingjing L, Charles X. Comparing naive Bayes, decision trees, and SVM with AUC and accuracy. 3rd IEEE International Conference on Data Mining (ICDM 2003); 2003 Nov 19-22: Melbourne (FL)
28. Guyon I, Gupta H. An introduction to variable and feature selection. J Mach Learn Res 2003; 3: 1157-82
29. Saeys Y, Degroeve S, Aeyels D, et al. Fast feature selection using a simple estimation of distribution algorithm: a case study on splice site prediction. Bioinformatics 2003; 19 Suppl. 2: ii179-88
30. Weiss GM. The effect of class disjuncts and class distribution on decision tree learning. New Jersey: Department of Computer Science, Rutgers University, 2003
31. Tan AC. Applications of ensemble machine learning to Bioinformatics [PhD thesis]. Glasgow: Department of Computing Science, University of Glasgow, 2004